It is a well-known challenge in prior art to reconstruct an image of a long object along the z direction from circular cone beam data using an iterative reconstruction (IR) technique. The challenges involve the reduction in axial artifacts that IR suffers due to truncated measured data. For example, if an object to be imaged is confined within a support cylinder as shown in FIG. 1, projection data is acquired by use of the conventional circular cone-beam scanning geometry. The image volumes irradiated by x-rays are defined by the rays p1, p2, p3, and p4 as a source S is rotated around a predetermined vertical axis Z. In this regard, shaded regions A are not sufficiently irradiated in the full-scan scheme since not all rays pass through the regions A within the angular range from 0 to 2π. Generally, iterative reconstruction algorithms fail to accurately reconstruct the region A due to the above described insufficiently measured data.
Furthermore, the iterative nature of IR contributes to the axial artifacts. Since IR uses reprojection, the axial coverage of the reconstruction field of view reduces after each instance of iteration. Still referring to FIG. 1, ray p5 is used to reconstruct a hexagonal region B by direct application of a predetermined iterative reconstruction algorithm. Unfortunately, because the ray p5 passes through both regions B and A, certain inaccuracy in the region A propagates into the region B through the use of the ray p5 through instances of the iteration process. Consequently, the reconstructed values are inaccurate in the region B.
For the above reasons, there remain need and desire for a method and system for substantially reducing axial artifacts in an image that has been reconstructed from cone beam data using an iterative reconstruction algorithm. For volume image reconstruction, an iterative algorithm includes a total variation (TV) minimization iterative reconstruction algorithm. Iterative reconstruction additionally involves Algebraic Reconstruction Technique (ART), Simultaneous Algebraic Reconstruction Technique (SART) or Ordered-subset Simultaneous Algebraic Reconstruction Technique (OS-SART).